基于信息融合的外绝缘状态检测方法研究

Fuchun Chen, Haifeng Jin, Gangjie Zhou, Lijun Jin, Pei Cao, Qi Liu
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引用次数: 0

摘要

电力系统外绝缘故障会造成巨大的损失,因此准确判断和预测外绝缘状态非常重要。现有的外绝缘状态检测方法都是基于放电产生的单一信号,容易误判,不利于故障的判断和预测。本文对受污染绝缘子进行了局部放电实验。通过对放电中释放的紫外和红外信号的采集、数据传输和信息融合,可以为外绝缘状态的判断提供支持。采用基于自适应网络的模糊推理系统(ANFIS)对两种信息进行信息融合,对绝缘子运行状态进行诊断。信息融合诊断的准确率可达92%,满足了电力系统外绝缘检测的要求。
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Research on External Insulation State Detection Method Based on Information Fusion
The external insulation fault of power system will cause huge loss, so it’s very important to accurately judge and predict the external insulation state. The existing detection methods of external insulation state are based on a single signal generated by discharge, which is prone to misjudgment and is not conducive to fault judgment and prediction. In this paper, the partial discharge experiment of contaminated insulator is carried out. Through the collection of the ultraviolet and infrared signals released in the discharge, data transmission and information fusion, it can provide support for the judgement of the state of the external insulation. The adaptive network-based fuzzy inference system (ANFIS) is used for information fusion of two kinds of information to diagnosis insulator running state. The accuracy of the information fusion diagnosis can reach 92%, which meets the requirements of the external insulation detection of the power system.
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